Cover Image

Non-parametric machine learning for pollinator image classification: a comparative study

Pollinators play a crucial role in maintaining the health of our planet's ecosystems by aiding in plant reproduction. However, identifying and differentiating between different types of pollinators can be a difficult task, especially when they have similar appearances. This difficulty in identi...

Full description

Saved in:
Bibliographic Details
Main Authors: Nasharuddin, Nurul Amelina, Zamri, Nurul Shuhada
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2024
Online Access:http://psasir.upm.edu.my/id/eprint/105623/1/2862
http://psasir.upm.edu.my/id/eprint/105623/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/3690
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.105623
record_format eprints
spelling my.upm.eprints.1056232024-05-09T03:20:31Z http://psasir.upm.edu.my/id/eprint/105623/ Non-parametric machine learning for pollinator image classification: a comparative study Nasharuddin, Nurul Amelina Zamri, Nurul Shuhada Pollinators play a crucial role in maintaining the health of our planet's ecosystems by aiding in plant reproduction. However, identifying and differentiating between different types of pollinators can be a difficult task, especially when they have similar appearances. This difficulty in identification can cause significant problems for conservation efforts, as effective conservation requires knowledge of the specific pollinator species present in an ecosystem. Thus, the aim of this study is to identify the most effective methods, features, and classifiers for developing a reliable pollinator classifier. Specifically, this initial study uses two primary features to differentiate between the pollinator types: shape and colour. To develop the pollinator classifiers, a dataset of 186 images of black ants, ladybirds, and yellow jacket wasps was collected. The dataset was then divided into training and testing sets, and four different non-parametric classifiers were used to train the extracted features. The classifiers used were the k-Nearest Neighbour, Decision Tree, Random Forest, and Support Vector Machine classifiers. The results showed that the Random Forest classifier was the most accurate, with a maximum accuracy of 92.11 when the dataset was partitioned into 80 training and 20 testing sets. By developing a reliable pollinator classifier, researchers and conservationists can better understand the roles of different pollinator species in maintaining ecosystem health. This understanding can lead to better conservation strategies to protect these important creatures, ultimately helping to preserve our planet's biodiversity. Semarak Ilmu Publishing 2024-03 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/105623/1/2862 Nasharuddin, Nurul Amelina and Zamri, Nurul Shuhada (2024) Non-parametric machine learning for pollinator image classification: a comparative study. Journal of Advanced Research in Applied Sciences and Engineering Technology, 34 (1). pp. 106-115. ISSN 2462-1943 https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/3690 10.37934/araset.34.1.106115
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Pollinators play a crucial role in maintaining the health of our planet's ecosystems by aiding in plant reproduction. However, identifying and differentiating between different types of pollinators can be a difficult task, especially when they have similar appearances. This difficulty in identification can cause significant problems for conservation efforts, as effective conservation requires knowledge of the specific pollinator species present in an ecosystem. Thus, the aim of this study is to identify the most effective methods, features, and classifiers for developing a reliable pollinator classifier. Specifically, this initial study uses two primary features to differentiate between the pollinator types: shape and colour. To develop the pollinator classifiers, a dataset of 186 images of black ants, ladybirds, and yellow jacket wasps was collected. The dataset was then divided into training and testing sets, and four different non-parametric classifiers were used to train the extracted features. The classifiers used were the k-Nearest Neighbour, Decision Tree, Random Forest, and Support Vector Machine classifiers. The results showed that the Random Forest classifier was the most accurate, with a maximum accuracy of 92.11 when the dataset was partitioned into 80 training and 20 testing sets. By developing a reliable pollinator classifier, researchers and conservationists can better understand the roles of different pollinator species in maintaining ecosystem health. This understanding can lead to better conservation strategies to protect these important creatures, ultimately helping to preserve our planet's biodiversity.
format Article
author Nasharuddin, Nurul Amelina
Zamri, Nurul Shuhada
spellingShingle Nasharuddin, Nurul Amelina
Zamri, Nurul Shuhada
Non-parametric machine learning for pollinator image classification: a comparative study
author_facet Nasharuddin, Nurul Amelina
Zamri, Nurul Shuhada
author_sort Nasharuddin, Nurul Amelina
title Non-parametric machine learning for pollinator image classification: a comparative study
title_short Non-parametric machine learning for pollinator image classification: a comparative study
title_full Non-parametric machine learning for pollinator image classification: a comparative study
title_fullStr Non-parametric machine learning for pollinator image classification: a comparative study
title_full_unstemmed Non-parametric machine learning for pollinator image classification: a comparative study
title_sort non-parametric machine learning for pollinator image classification: a comparative study
publisher Semarak Ilmu Publishing
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/105623/1/2862
http://psasir.upm.edu.my/id/eprint/105623/
https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/view/3690
_version_ 1800093781140176896
score 13.211869

Similar Items